#!/usr/bin/env python

# --------------------------------------------------------
# Fast R-CNN
# Copyright (c) 2015 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ross Girshick
# Modified by Xingyi Zhou
# --------------------------------------------------------

# Reval = re-eval. Re-evaluate saved detections.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import sys
import pickle
import os
import argparse
import json
from .voc_eval_lib.model.test import apply_nms
from .voc_eval_lib.datasets.pascal_voc import pascal_voc


def parse_args():
    """
    Parse input arguments
    """
    parser = argparse.ArgumentParser(description='Re-evaluate results')
    parser.add_argument('detection_file', type=str)
    parser.add_argument('--output_dir', help='results directory', type=str)
    parser.add_argument('--imdb', dest='imdb_name',
                        help='dataset to re-evaluate',
                        default='voc_2007_test', type=str)
    parser.add_argument('--matlab', dest='matlab_eval',
                        help='use matlab for evaluation',
                        action='store_true')
    parser.add_argument('--comp', dest='comp_mode', help='competition mode',
                        action='store_true')
    parser.add_argument('--nms', dest='apply_nms', help='apply nms',
                        action='store_true')

    if len(sys.argv) == 1:
        parser.print_help()
        sys.exit(1)

    args = parser.parse_args()
    return args


def from_dets(imdb_name, detection_file, args):
    imdb = pascal_voc('test', '2007')
    imdb.competition_mode(args.comp_mode)
    imdb.config['matlab_eval'] = args.matlab_eval
    with open(os.path.join(detection_file), 'rb') as f:
        if 'json' in detection_file:
            dets = json.load(f)
        else:
            dets = pickle.load(f, encoding='latin1')
    # import pdb; pdb.set_trace()
    if args.apply_nms:
        print('Applying NMS to all detections')
        test_nms = 0.3
        nms_dets = apply_nms(dets, test_nms)
    else:
        nms_dets = dets

    print('Evaluating detections')
    imdb.evaluate_detections(nms_dets)


if __name__ == '__main__':
    args = parse_args()

    imdb_name = args.imdb_name
    from_dets(imdb_name, args.detection_file, args)
